Penalty guided genetic search for reliability design optimization

Reliability optimization has been studied in the literature for decades, usually using a mathematical programming approach. Because of these solution methodologies, restrictions on the type of allowable design have been made, however heuristic optimization approaches are free of such binding restrictions. One difficulty in applying heuristic approaches to reliability design is the highly constrained nature of the problems, both in terms of number of constraints and the difficulty of satisfying constraints. This paper presents a penalty guided genetic algorithm which efficiently and effectively searches over promising feasible and infeasible regions to identify a final, feasible optimal, or near optimal, solution. The penalty function is adaptive and responds to the search history. Results obtained on 33 test problems from the literature dominate previous solution techniques.